A widely adopted DP mechanism in deep learning is DP-Stochastic Gradient Descent (DP-SGD) [1]} wherein noise is added to clipped gradients during training.
Although DP-SGD offers rigorous privacy guarantees, it degrades the accuracy of the resulting model.
Furthermore, it has recently been shown that this degradation is disparate, and that differential privacy exacerbates the already existing gap between the utility of the model for under-represented and well-represented subgroups of data [2]}, [3]}, [4]}.
Given how machine learning models are deployed in real life and are sometimes used to make high-stake decisions such as who to hire for a role or how much someone should pay for insurance, it is paramount that we find, acknowledge and mitigate biases induced by different algorithms, in this case DP.
